Spar: A programming language for semi-automatic compilation of parallel programs

Reeuwijk, Kees van; Gemund, Arjan J. C. van; Sips, Henk

doi:10.1002/(sici)1096-9128(199711)9:11<1193::aid-cpe334>3.0.co;2-b

Cited by 29 publications

(18 citation statements)

References 11 publications

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“…Specifying code segments as being implemented according to particular parallelizable patterns relieves the compiler of extensive dependency analysis, and allows for lazy parallelization to be incorporated. Currently, this approach is being considered for the SPAR parallel language [24], [31].…”

Section: Applicabilitymentioning

confidence: 99%

Finite state machine-based optimization of data parallel regular domain problems applied in low-level image processing

Seinstra

Koelma

Bagdanov

2004

IEEE Trans. Parallel Distrib. Syst.

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A popular approach to providing nonexperts in parallel computing with an easy-to-use programming model is to design a software library consisting of a set of preparallelized routines, and hide the intricacies of parallelization behind the library's API. However, for regular domain problems (such as simple matrix manipulations or low-level image processing applications-in which all elements in a regular subset of a dense data field are accessed in turn) speedup obtained with many such library-based parallelization tools is often suboptimal. This is because interoperation optimization (or: time-optimization of communication steps across library calls) is generally not incorporated in the library implementations. This paper presents a simple, efficient, finite state machine-based approach for communication minimization of library-based data parallel regular domain problems. In the approach, referred to as lazy parallelization, a sequential program is parallelized automatically at runtime by inserting communication primitives and memory management operations whenever necessary. Apart from being simple and cheap, lazy parallelization guarantees to generate legal, correct, and efficient parallel programs at all times. The effectiveness of the approach is demonstrated by analyzing the performance characteristics of two typical regular domain problems obtained from the field of low-level image processing. Experimental results show significant performance improvements over nonoptimized parallel applications. Moreover, obtained communication behavior is found to be optimal with respect to the abstraction level of message passing programs.

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Section: Applicabilitymentioning

confidence: 99%

Finite state machine-based optimization of data parallel regular domain problems applied in low-level image processing

Seinstra

Koelma

Bagdanov

2004

IEEE Trans. Parallel Distrib. Syst.

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“…Other approaches to providing parallel programming interfaces for Java include JavaParty [12], HPJava [9], Titanium [14] and SPAR Java [13]. These are also in the research phase and, in addition, require genuine language extensions.…”

Section: Introductionmentioning

confidence: 99%

A parallel java grande benchmark suite

Smith

Bull

Obdrizalek

2001

Proceedings of the 2001 ACM/IEEE Conference on Supercomputing

217

145

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Increasing interest is being shown in the use of Java for large scale or Grande applications. This new use of Java places specific demands on the Java execution environments that can be tested using the Java Grande benchmark suite [5], [6], [7]. The large processing requirements of Grande applications makes parallelisation of interest. A suite of parallel benchmarks has been developed from the serial Java Grande benchmark suite, using three parallel programming models: Java native threads, MPJ (a message passing interface) and JOMP (a set of OpenMP-like directives). The contents of the suite are described, and results presented for a number of platforms.

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“…Timber [26] is a Java-based programming language for semi-automatic array-parallel programming, in particular for programming array-based applications. The language has been designed as part of the Automap project, in which a compiler and run-time system are being developed for distributed-memory systems.…”

Section: Timbermentioning

confidence: 99%

HP Java: Programming Support for High-Performance Grid-Enabled Applications

Lee

Carpenter

Fox

et al. 2004

Parallel Algorithms and Applications

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The paper begins by considering what a Grid Computing Environment might be, why it is demanded, and how the authors' HPspmd programming fits into this picture. We then review our HPJava environment 1 as a contribution towards programming support for High-Performance GridEnabled Environments. Future grid computing systems will need to provide programming models. In a proper programming model for grid-enabled environments and applications, high performance on multi-processor systems is a critical issue. We describe the features of HPJava, including run-time communication library, compilation strategies, and optimization schemes. Through experiments, we compare HPJava programs against FORTRAN and ordinary Java programs. We aim to demonstrate that HPJava can be used "anywhere"-not only for high-performance parallel computing, but also for grid-enabled applications.

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Spar: A programming language for semi-automatic compilation of parallel programs

Cited by 29 publications

References 11 publications

Finite state machine-based optimization of data parallel regular domain problems applied in low-level image processing

Finite state machine-based optimization of data parallel regular domain problems applied in low-level image processing

A parallel java grande benchmark suite

HP Java: Programming Support for High-Performance Grid-Enabled Applications

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